Move Forward and Tell: A Progressive Generator of Video Descriptions

We present an efficient framework that can generate a coherent paragraph to describe a given video. Previous works on video captioning usually focus on video clips. They typically treat an entire video as a whole and generate the caption conditioned on a single embedding. On the contrary, we consider videos with rich temporal structures and aim to generate paragraph descriptions that can preserve the story flow while being coherent and concise. Towards this goal, we propose a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions. Given a video, it selects a sequence of distinctive clips and generates sentences thereon in a coherent manner. Particularly, the selection of clips and the production of sentences are done jointly and progressively driven by a recurrent network -- what to describe next depends on what have been said before. Here, the recurrent network is learned via self-critical sequence training with both sentence-level and paragraph-level rewards. On the ActivityNet Captions dataset, our method demonstrated the capability of generating high-quality paragraph descriptions for videos. Compared to those by other methods, the descriptions produced by our method are often more relevant, more coherent, and more concise.Comment: Accepted by ECCV 201

Risk-Based Capacitor Placement in Distribution Networks

In this paper, the problem of sizing and placement of constant and switching capacitors in electrical distribution systems is modelled considering the load uncertainty. This model is formu- lated as a multicriteria mathematical problem. The risk of voltage violation is calculated, and the stability index is modelled using fuzzy logic and fuzzy equations. The instability risk is introduced as the deviation of our fuzzy-based stability index with respect to the stability margin. The capacitor placement objectives in our paper include: (i) minimizing investment and installation costs as well as loss cost; (ii) reducing the risk of voltage violation; and (iii) reducing the instability risk. The proposed mathematical model is solved using a multi-objective version of a genetic algorithm. The model is implemented on a distribution network, and the results of the experiment are discussed. The impacts of constant and switching capacitors are assessed separately and concurrently. Moreo- ver, the impact of uncertainty on the multi-objectives is determined based on a sensitivity analysis. It is demonstrated that the more the uncertainty is, the higher the system cost, the voltage risk and the instability risk are

Prestressing wire breakage monitoring using sound event detection

Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel-frequency cepstral coefficients (MFCCs) and back-propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real-world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over-fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real-time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real-world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real-time monitoring of bridges, ultimately leading to safer and longer-lasting infrastructure

Evaluation of diagnostic value of soluble urokinase-type plasminogen activator receptor in sepsis

Background: Sepsis is one of the most important causes of morbidity and mortality in the intensive care units (ICUs). It is difficult to accurately differentiate sepsis from similar diseases rapidly. Therefore, it becomes critical to identify any biomarker with the ability of differentiation between sepsis and nonsepsis conditions. The urokinase plasminogen activator receptor has been implicated as an important factor in regulation of leukocyte adhesion and migration. Objectives: In this study, we evaluated the value of soluble urokinase plasminogen activator receptor (suPAR), erythrocyte sedimentation (ESR), and C-reactive protein (CRP) serum levels in terms of their value for sepsis diagnosis in ICU patients. Patients and Methods: We enrolled 107 ICU patients; 40 with sepsis, 43 with systemic inflammatory response syndrome, and 24 as control group. Serum soluble urokinase plasminogen activator receptor, ESR, white blood cell (WBC), and CRP levels were measured on the day of admission. Results: The group with sepsis had higher suPAR, ESR, and CRP levels compared with the group with noninfectious systemic inflammatory response syndrome (SIRS) (P = 0.01, 0.00 and 0.00, respectively). CRP concentrations and ESR were higher in the sepsis group than in the non-SIRS group (P = 0.00 and 0.00, respectively). In a receiver-operating characteristic curve analysis, ESR, CRP and suPAR had an area under the curve larger than 0.65 (P = 0.00) in distinguishing between septic and noninfectious SIRS patients. CRP, ESR and suPAR had a sensitivity of 87, 71 and 66 and a specificity of 59, 76 and 74 respectively in diagnosing infection in SIRS. Conclusions: The diagnostic values of CRP and ESR were better than suPAR and WBC count in patients with sepsis. © 2015, Infectious Diseases and Tropical Medicine Research Center

Endoscopic repair of transsellar transsphenoidal meningoencephalocele; Case report and review of approaches

We present an extremely rare case of transsellar transsphenoidal meningoencephalocele in a 36-year-old woman with pituitary dwarfism complaining of nasal obstruction. Imaging studies showed a bony defect in the sellar floor and sphenoid sinus with huge nasopharyngeal mass and 3rd ventricle involvement. Using endoscopic endonasal approach the sac was partially removed and the defect was reconstructed with fat and fascial graft, and buttressed with titanium mesh and septal flap. Visual field improvement was noticed post-operatively and no complication was encountered during follow-up. So, endoscopic endonasal approach with partial resection of the sac is a safe and effective treatment for this disease. © 2015 The Authors. Published by Elsevier B.V

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission